The present invention relates generally to computer database systems, and specifically to methods for organizing information from one or more systems in a data warehousing environment.
Few could foresee the rapid development of computer technology just a few years ago. Computers now have a place in our homes, our offices, our schools and even the our briefcases and satchels. As computer automation continues to impact an ever increasing portion of our daily lives, governments, businesses and individuals have turned to database technology to help them manage the “information explosion” and the exponential proliferation of information that must be sorted, assimilated and managed on a continuing basis. One area of importance to the database design field is data model selection for database applications.
A data model represents the structure or organization of data stored in the database. It enables the use of data in certain forms and may limit the data being used in other forms. Different applications usually require different data models. Many different data models can exist, and they usually differ markedly from one another. Typically, database applications are customized to a particular data model of a particular database. Different database vendors base their products on different data models, adding to the confusion. Usually, these applications must be re-implemented for different databases, even though the functioning of the application remains the same.
Presently, database developers have turned to data warehousing technology to resolve often conflicting data management requirements. Traditional data warehousing approaches focus on decision support applications, which emphasize summarized information. While perceived advantages exist, an inherent disadvantage to these systems is that transaction details about the customer's identity are lost. Traditional approaches exhibit shortcomings when applied to applications such as customer data analysis. Customer data analysis is a decision support analysis that correlates data to customers' activities, events, transactions, status and the like. Summarized information usually loses the detail level of information about customer identity, limiting the usefulness of traditional data warehousing approaches in these types of applications.
What is needed is a method for providing a database that can be customized to fit individual user needs, yet also able to support data analysis applications.